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Abstract

Seasonal prediction systems based on Earth System Models exhibit a lower proportion of predictable signal to unpredictable noise than the actual world. This puzzling phenomena has been widely referred to as the signal-to-noise paradox (SNP). Here, we investigate the SNP in a conceptual framework of a seasonal prediction system based on the Lorenz, 1963 Model (L63). We show that the SNP is not apparent in L63, if the uncertainty assumed for the initialization of the ensemble is equal to the uncertainty in the starting conditions. However, if the uncertainty in the initialization overestimates the uncertainty in the starting conditions, the SNP is apparent. In these experiments the metric used to quantify the SNP also shows a clear lead-time dependency on subseasonal timescales. We therefore, formulate the alternative hypothesis to previous studies that the SNP could also be related to the magnitude of the initial ensemble spread.
Plain Language Summary Comprehensive Earth System Models seem to be better at predicting the real observed climate system than expected based on their ability to predict their own modelled climate system. This puzzling phenomena is known as the signal-to-noise paradox (SNP) and its origin is still under intensive scientific debate with some studies pointing to deficiencies in the model formulation. In this study we investigate under which conditions the SNP can be obtained using a simple conceptual framework for a climate prediction system based on a simple dynamical model. Our results show that the SNP can be reproduced in the absence of model deficiencies if the model overestimates the observational uncertainty. We also investigate the development of the SNP on subseasonal timescales and find a clear dependency on the lead-time of the prediction. Our results lead us to formulate an alternative hypothesis to previous studies on the origin of the SNP.